Data is a prime driver for organisations, but a dearth of adequate data can hamper the analytics process. As data is usually not available off-the-peg, professionals extract it from different sources. Since information resides in various structure and formats, data scientists deploy web crawler and obtain the necessary information.
Data scientists use different third-party libraries that are available in the market to effortlessly perform web scraping and gather data. However, with the varying needs, they can opt for different libraries for simplifying workflows. Every library has its own advantages, thus depending on the requirements, one can leverage one over the others.
Libraries In Python
Scrapy: It is a web scraping framework that encompasses every requirement of data gathering from webpages. This makes it suitable for large projects to handle a load of continuous crawling through the asynchronous feature. Moreover, it facilitates professionals to collected data into several formats like JSON, JSON Lines, XML, and CSV. This empowers users to structure the data and expedite the processes. Scrapy is an open-source project and is constantly enhanced by contributors from around the world.
Selenium: The facet of Selenium is that it supports javascript parsing, which is not available on other libraries. However, with Scrapy one can render javascript but it requires to import Splash library. It can be embraced in small projects that due to the absence of asynchronous options. But one of the few disadvantages of this library is its documentation; many feel that it is cumbersome to navigate and find relevant examples.
Requests and Beautiful Soup: These two libraries are often used in tandem with one another for web scraping. While the Requests is used to make a request for getting Html of web pages, the beautiful soup is embraced to parse the Html into soup object that helps in finding data. These can be used for one-off projects that are only deployed for collecting data from a few webpages. Requests and Beautiful Soup are ideal for projects involving scraping from 1000 pages or less. It will be better off to use other libraries in case the crawling needs more than a thousand pages.
These libraries do not support asynchronous, thereby does not automate the Html requests. Besides, to store the data, you will have to create your own data structure as it does not include built-in data storage. Further, if you want to render javascript, you will have to embrace Selenium.
Libraries in R Programming
RCrawler: The crawler is similar to what Scrapy offers in Python, it allows users to crawl, retrieve, and parse. Unlike other packages that do not provide crawling, RCrawler can be deployed to continuously mine data from websites. Such features help professionals to utilise it in hug projects but due to the limitation of R in handling large data while crawling.
Rvest: Inspired by BeautifulSoup, Rvest was written by Hadley Wickham, Chief Scientist at RStudio. It only includes parsing and retrieving, thus crawling cannot be used to harvest data from webpages. Rvest in the background works with magrittr to render complex operations while web scraping.
Outlook
Bringing Python into service can derive more value for your web scraping projects as the libraries are more task-specific. This assists in decreasing the resources that a project needs for its successful implementation. Besides, Python has many libraries than what R offers for screen scraping. Such flexibility allows you to effectively chose among libraries for addressing the data gap in your projects.
While there are other libraries in both Python and R, we picked only the most popular ones to shred some light on aspects you should consider while determining the best module and programing language based on the project.
